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Linear Algebra and Matlab tutorial

1. Introduction to MATLAB
2. Linear algebra refresher
3. Writing fast MATLAB code

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Linear Algebra and Matlab tutorial

  1. 1. Linear Algebra and MATLAB Tutorial Jia-Bin Huang University of Illinois, Urbana-Champaign www.jiabinhuang.com jbhuang1@Illinois.edu
  2. 2. Today’s class • Introduction to MATLAB • Linear algebra refresher • Writing fast MATLAB code
  3. 3. Working with MATLAB
  4. 4. Learning by doing • Let’s work through an example! • Download the example script here • Topics • Basic types in Matlab • Operations on vectors and matrices • Control statements & vectorization • Saving/loading your work • Creating scripts or functions using m-files • Plotting • Working with images
  5. 5. Questions?
  6. 6. Linear algebra refresher • Linear algebra is the math tool du jour • Compact notation • Convenient set operations • Used in all modern texts • Interfaces well with MATLAB, numpy, R, etc. • We will use a lot of it!! Slides credits: Paris Smaragdis and Karianne Bergen
  7. 7. Filtering, linear transformation -> extracting frequency sub-bands Vector dot products -> measure patch similarities Hybrid Image Image Quilting Gradient Domain Fusion Solving linear systems -> solve for pixel values Image-based Lighting Solving linear systems -> solve for radiance map Video Stitching and Processing Solving linear systems -> solve for geometric transform
  8. 8. Scalars, Vectors, Matrices, Tensors
  9. 9. How will we see these? • Vector: an ordered collection of scalars (e.g., sounds) • Matrix: a two-dimensional collection of scalars (e.g., images) • Tensor: 3D signals (e.g., videos)
  10. 10. Element-wise operations • Addition/subtraction • 𝒂 ± 𝒃 = 𝒄 ⇒ 𝑎𝑖 + 𝑏𝑖 = 𝑐𝑖 • Multiplication (Hadamard product) • 𝒂 𝒃 = 𝒄 ⇒ 𝑎𝑖 𝑏𝑖 = 𝑐𝑖 • No named operator for element-wise division • Just use Hadamard with inverted elements c = a + b; c = a – b; c = a.*b; c = a./b;
  11. 11. Which division? Left Right Array division C = ldivide(A, B); C = A.B; C = rdivide(A, B); C = A./B; Matrix division C = mldivide(A, B); C = A/B; C = mldivide(A, B); C = AB; EX: Array division A = [1 2; 3, 4]; B = [1,1; 1, 1]; C1 = A.B; C2 = A./B; C1 = 1.0000 0.5000 0.3333 0.2500 C2 = 1 2 3 4 EX: Matrix division X = AB; -> the (least-square) solution to AX = B X = A/B; -> the (least-square) solution to XB = A B/A = (A'B')'
  12. 12. Transpose • Change rows to columns (and vice versa) • Hermitian (conjugate transpose) • Notated as 𝑋H • Y = X’; % Hermitian transpose • Y = X.’; % Transpose (without conjugation)
  13. 13. Visualizing transposition • Mostly pointless for 1D signals • Swap dimensions for 2D signals
  14. 14. Reshaping operators • The vec operator • Unrolls elements column-wise • Useful for getting rid of matrices/tensors • B = A(:); • The reshape operator • B = reshape(A, sz); • prod(sz) must be the same as numel(A) EX: Reshape A = 1:12; B = reshape(A,[3,4]); B = 1 4 7 10 2 5 8 11 3 6 9 12
  15. 15. Trace and diag • Matrix trace • Sum of diagonal elements • The diag operator EX: trace A = [1, 2; 3, 4]; t = trace(A); Q: What’s t? EX: diag x = diag(A); D = diag(x); Q: What’s x and D?
  16. 16. Vector norm (how “big" a vector is) • Taxicab norm or Manhattan norm • | 𝑣 | 1 = ∑|𝑣𝑖| • Euclidean norm • | 𝑣 | 2 = ∑𝑣𝑖 2 1 2 = 𝑣⊤ 𝑣 • Infinity norm • | 𝑣 | ∞ = max 𝑖 |𝑣𝑖| • P-norm • | 𝑣 | 𝑝 = ∑𝑣𝑖 𝑝 1 𝑝 Unit circle (the set of all vectors of norm 1)
  17. 17. Which norm to use? • Say residual 𝑣 = (measured value) – (model estimate) • | 𝑣 | 1: if you want to your measurement and estimate to match exactly for as many samples as possible • | 𝑣 | 2: use this if large errors are bad, but small errors are ok. • | 𝑣 | ∞: use this if no error should exceed a prescribed value .
  18. 18. Vector-vector products • Inner product 𝐱⊤ 𝒚 • Shorthand for multiply and accumulate 𝐱⊤ 𝒚 = 𝒊 𝑥𝑖 𝑦𝑖 = 𝐱 𝐲 cos 𝜃 • Outer product: 𝐱𝒚⊤
  19. 19. Matrix-vector product • Generalizing the dot product • Linear combination of the columns of A
  20. 20. Matrix-matrix product C = AB Definition: as inner products 𝐶𝑖𝑗 = 𝑎𝑖 ⊤ 𝑏𝑗 As sum of outer productsAs a set of matrix-vector products • matrix 𝐴 and columns of B • rows of 𝐴 and matrix B
  21. 21. Matrix products • Output rows == left matrix rows • Output columns == right matrix columns
  22. 22. Matrix multiplication properties • Associative: 𝐴𝐵 𝐶 = 𝐴(𝐵𝐶) • Distributive: 𝐴 𝐵 + 𝐶 = 𝐴𝐵 + 𝐴𝐶 • NOT communitive: 𝐴𝐵 ≠ 𝐵𝐴
  23. 23. Linear independence • Linear dependence: a set of vectors v1, v2, ⋯ , 𝑣𝑟 is linear dependent if there exists a set of scalars 𝛼1, 𝛼2, ⋯ , 𝛼 𝑟 ∈ ℝ with at least one 𝛼𝑖 ≠ 0 such that 𝛼1 𝑣1 + 𝛼2 𝑣2 + ⋯ + 𝛼 𝑟 𝑣𝑟 = 0 i.e., one of the vectors in the set can be written as a linear combination of one or more other vectors in the set. • Linear independence: a set of vectors v1, v2, ⋯ , 𝑣𝑟 is linear independent if it is NOT linearly dependent. 𝛼1 𝑣1 + 𝛼2 𝑣2 + ⋯ + 𝛼 𝑟 𝑣𝑟 = 0 ⟺ 𝛼𝑖 = ⋯ = 𝛼 𝑟 = 0
  24. 24. Basis and dimension • Basis: a basis for a subspace 𝑆 is a linear independent set of vectors that span 𝑆 • 1 0 , 0 1 and 1 −1 , 1 1 are both bases that span ℝ2 • Not unique • Dimension dim(𝑆): the number of linearly independent vectors in the basis for 𝑆
  25. 25. Range and nullspace of a matrix 𝐴 ∈ ℝ 𝑚×𝑛 • The range (column space, image) of a matrix 𝐴 ∈ ℝ 𝑚×𝑛 • Denoted by ℛ(𝐴) • The set of all linear combination of the columns of 𝐴 ℛ 𝐴 = 𝐴𝑥 𝑥 ∈ ℝ 𝑛 }, ℛ(𝐴) ⊆ ℝ 𝑚 • The nullspace (kernel) of a matrix 𝐴 ∈ ℝ 𝑚×𝑛 • Denoted by 𝒩(𝐴) • The set of vectors z such that 𝐴𝑧 = 0 𝒩 𝐴 = { 𝑧 ∈ ℝ 𝑛|𝐴𝑧 = 0}, 𝒩 𝐴 ⊆ ℝ 𝑚
  26. 26. Rank of 𝐴 ∈ ℝ 𝑚×𝑛 • Column rank of 𝐴: dimension of ℛ(𝐴) • Row rank of 𝐴: dimension of ℛ 𝐴⊤ • Column rank == row rank • Matrix 𝐴 ∈ ℝ 𝑚×𝑛 is full rank if 𝑟𝑎𝑛𝑘 𝐴 = min {𝑚, 𝑛 }
  27. 27. System of linear equations • Ex: Find values 𝑥1, 𝑥2, 𝑥3 ∈ ℝ that satisfy • 3𝑥1 + 2𝑥2 − 𝑥3 − 1 = 0 • 2𝑥1 − 2𝑥2 + 𝑥3 + 2 = 0 • −𝑥1 − 1 2 𝑥2 − 𝑥3 = 0 Solution: • Step 1: write the system of linear equations as a matrix equation 𝐴 = 3 2 −1 2 −2 1 −1 − 1 2 −1 , 𝑥 = 𝑥1 𝑥2 𝑥3 , 𝑏 = 1 −2 0 . • Step 2: Solve for 𝐴𝑥 = 𝑏
  28. 28. Mini-quiz 1 • What’s the 𝐴, 𝑥, and 𝑏 for the following linear equations? • 2𝑥2 − 7 = 0 • 2𝑥1 − 3𝑥3 + 2 = 0 • 4𝑥2 + 2𝑥3 = 0 • 𝑥1 + 3𝑥3 = 0 𝐴 = 0 1 0 2 0 −3 0 1 4 0 2 3 , 𝑥 = 𝑥1 𝑥2 𝑥3 , 𝑏 = 7 −2 0 0 .
  29. 29. Mini-quiz 2 • What’s the 𝐴, 𝑥, and 𝑏 for the following linear equation? • 𝑋 = 𝑥1 𝑥3 𝑥2 𝑥4 • 1 2 ⊤ 𝑋 −2 1 − 3 = 0 • 1 0 ⊤ 𝑋 1 1 + 1 = 0 𝐴 = −2 − 4 1 2 1 0 1 0 , 𝑥 = 𝑥1 𝑥2 𝑥3 𝑥4 , 𝑏 = 3 −1
  30. 30. Solving system of linear equations • Given 𝐴 ∈ ℝ 𝑚×𝑛 and b ∈ ℝ 𝑚, find x ∈ ℝ 𝑛 such that 𝐴𝑥 = 𝑏 • Special case: a square matrix 𝐴 ∈ ℝ 𝑛×𝑛 • The solution: 𝐴−1 𝑏 • The matrix inverse A−1exists and is unique if and only if • 𝐴 is a squared matrix of full rank • “Undoes” a matrix multiplication • 𝐴−1 𝐴 = 𝐼 • 𝐴−1 𝐴𝑌 = 𝑌 • Y𝐴𝐴−1 = 𝑌
  31. 31. Least square problems • What if 𝑏 ∉ ℛ(𝐴)? • No solution 𝑥 exists such that 𝐴𝑥 = 𝑏 • Least Squares problem: • Define residual 𝑟 = 𝐴𝑥 − 𝑏 • Find vector 𝑥 ∈ ℝ 𝑛 that minimizes ||𝑟||2 2 =||𝐴𝑥 − 𝑏||2 2
  32. 32. Least square problems • Decompose b ∈ ℝm into components b = b1 + b2 with • 𝑏1 ∈ ℛ(𝐴) and • 𝑏2 ∈ 𝒩 A⊤ = ℛ A ⊥ . • Since 𝑏2 is in ℛ A ⊥ , the orthogonal complement of ℛ A , the residual norm ||𝑟||2 2 = ||𝐴𝑥 − 𝑏1 − 𝑏2||2 2 = ||𝐴𝑥 − 𝑏1||2 2 + ||𝑏2||2 2 which is minimized when 𝐴𝑥 = 𝑏1 and 𝑟 = 𝑏2 ∈ 𝒩 A⊤
  33. 33. Normal equations • The least squares solution 𝑥 occurs when 𝑟 = 𝑏2 ∈ 𝒩 A⊤ , or equivalently 𝐴⊤ 𝑟 = 𝐴⊤ 𝑏 − 𝐴𝑥 = 0 • Normal equations: 𝐴⊤ 𝐴𝑥 = 𝐴⊤ 𝑏 • If 𝐴 has full column rank, then 𝐴⊤ 𝐴 is invertible. • Thus (𝐴⊤ 𝐴)𝑥 = (𝐴⊤ 𝑏) has a unique solution 𝑥 = (𝐴⊤ 𝐴)−1 𝐴⊤ 𝑏 • x = Ab; • x = inv(A’*A)*A’*b; % same as backslash operator
  34. 34. Questions?
  35. 35. Writing Fast MATLAB Code
  36. 36. Using the Profiler • Helps uncover performance problems • Timing functions: • tic, toc • The following timings were measured on - CPU i5 1.7 GHz - 4 GB RAM • http://www.mathworks.com/help/matlab/ref/profile.html
  37. 37. Pre-allocation Memory 3.3071 s >> n = 1000; 2.1804 s2.5148 s
  38. 38. Reducing Memory Operations >> x = 4; >> x(2) = 7; >> x(3) = 12; >> x = zeros(3,1); >> x = 4; >> x(2) = 7; >> x(3) = 12;
  39. 39. Vectorization 2.1804 s 0.0157 s 139x faster!
  40. 40. Using Vectorization • Appearance • more like the mathematical expressions, easier to understand. • Less Error Prone • Vectorized code is often shorter. • Fewer opportunities to introduce programming errors. • Performance: • Often runs much faster than the corresponding code containing loops. See http://www.mathworks.com/help/matlab/matlab_prog/vectorization.html
  41. 41. Binary Singleton Expansion Function • Make each column in A zero mean >> n1 = 5000; >> n2 = 10000; >> A = randn(n1, n2); • See http://blogs.mathworks.com/loren/2008/08/04/comparing-repmat-and-bsxfun- performance/ 0.2994 s 0.2251 s Why bsxfun is faster than repmat? - bsxfun handles replication of the array implicitly, thus avoid memory allocation - Bsxfun supports multi-thread
  42. 42. Loop, Vector and Boolean Indexing • Make odd entries in vector v zero • n = 1e6; • See http://www.mathworks.com/help/matlab/learn_matlab/array-indexing.html • See Fast manipulation of multi-dimensional arrays in Matlab by Kevin Murphy 0.3772 s 0.0081 s 0.0130 s
  43. 43. Solving Linear Equation System 0.1620 s 0.0467 s
  44. 44. Dense and Sparse Matrices • Dense: 16.1332 s • Sparse: 0.0040 s More than 4000x faster! Useful functions: sparse(), spdiags(), speye(), kron().0.6424 s 0.1157 s
  45. 45. Repeated solution of an equation system with the same matrix 3.0897 s 0.0739 s 41x faster!
  46. 46. Iterative Methods for Larger Problems • Iterative solvers in MATLAB: • bicg, bicgstab, cgs, gmres, lsqr, minres, pcg, symmlq, qmr • [x,flag,relres,iter,resvec] = method(A,b,tol,maxit,M1,M2,x0) • source: Writing Fast Matlab Code by Pascal Getreuer
  47. 47. Solving Ax = b when A is a Special Matrix • Circulant matrices • Matrices corresponding to cyclic convolution Ax = conv(h, x) are diagonalized in the Fourier domain >> x = ifft( fft(b)./fft(h) ); • Triangular and banded • Efficiently solved by sparse LU factorization >> [L,U] = lu(sparse(A)); >> x = U(Lb); • Poisson problems • See http://www.cs.berkeley.edu/~demmel/cs267/lecture25/lecture25.html
  48. 48. In-place Computation >> x=randn(1000,1000,50); 0.1938 s 0.0560 s 3.5x faster!
  49. 49. Inlining Simple Functions 1.1942 s 0.3065 s functions are worth inlining: - conv, cross, fft2, fliplr, flipud, ifft, ifft2, ifftn, ind2sub, ismember, linspace, logspace, mean, median, meshgrid, poly, polyval, repmat, roots, rot90, setdiff, setxor, sortrows, std, sub2ind, union, unique, var y = medfilt1(x,5); 0.2082 s
  50. 50. Using the Right Type of Data “Do not use a cannon to kill a mosquito.” double image: 0.5295 s uint8 image: 0.1676 s Confucius
  51. 51. Matlab Organize its Arrays as Column-Major • Assign A to zero row-by-row or column-by-column >> n = 1e4; >> A = randn(n, n); 0.1041 s2.1740 s 21x faster!
  52. 52. Column-Major Memory Storage >> x = magic(3) x = 8 1 6 3 5 7 4 9 2 % Access one column >> y = x(:, 1); % Access one row >> y = x(1, :);
  53. 53. Copy-on-Write (COW) >> n = 500; >> A = randn(n,n,n); 0.4794 s 0.0940 s
  54. 54. Clip values >> n = 2000; >> lowerBound = 0; >> upperBound = 1; >> A = randn(n,n); 0.0121 s0.1285 s 10x faster!
  55. 55. Moving Average Filter • Compute an N-sample moving average of x >> n = 1e7; >> N = 1000; >> x = randn(n,1); 3.2285 s 0.3847 s
  56. 56. Find the min/max of a matrix or N-d array >> n = 500; >> A = randn(n,n,n); 0.5465 s 0.1938 s
  57. 57. Acceleration using MEX (Matlab Executable) • Call your C, C++, or Fortran codes from the MATLAB • Speed up specific subroutines • See http://www.mathworks.com/help/matlab/matlab_external/introducing-mex- files.html
  58. 58. MATLAB Coder • MATLAB Coder™ generates standalone C and C++ code from MATLAB® code • See video examples in http://www.mathworks.com/products/matlab- coder/videos.html • See http://www.mathworks.com/products/matlab-coder/
  59. 59. DoubleClass • http://documents.epfl.ch/users/l/le/leuteneg/www/MATLABToolbox/ DoubleClass.html
  60. 60. parfor for parallel processing • Requirements • Task independent • Order independent See http://www.mathworks.com/products/parallel-computing/
  61. 61. Parallel Processing in Matlab • MatlabMPI • multicore • pMatlab: Parallel Matlab Toolbox • Parallel Computing Toolbox (Mathworks) • Distributed Computing Server (Mathworks) • MATLAB plug-in for CUDA (CUDA is a library that used an nVidia board) • Source: http://www-h.eng.cam.ac.uk/help/tpl/programs/Matlab/faster_scripts.html
  62. 62. Resources for your final projects • Awesome computer vision by Jia-Bin Huang • A curated list of computer vision resources • VLFeat • features extraction and matching, segmentation, clustering • Piotr's Computer Vision Matlab Toolbox • Filters, channels, detectors, image/video manipulation • OpenCV (MexOpenCV by Kota Yamaguchi) • General purpose computer vision library
  63. 63. Resources • Linear Algebra • Linear Algebra Review and Reference • Linear algebra refresher course • Quick Review of Matrix and Real Linear Algebra • MATLAB Tutorial • Resource collection • MATLAB F&Q • Writing Fast MATLAB code • Techniques for Improving Performance by Mathwork • Writing Fast Matlab Code by Pascal Getreuer • Guidelines for writing clean and fast code in MATLAB by Nico Schlömer

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